提出可控生成方法，提升人脸验证的公平性与平衡性。
Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
- 设计新生成流程，通过控制条件实现更公平的人脸合成
- 在多个公平性指标上优于现有方法，性能略有提升
- 适合关注人脸识别公平性的研究者与开发者
人脸识别与验证任务因深度表征的发展而取得显著进展，但面部数据的敏感性及真实训练数据中的偏差带来了伦理、法律和技术挑战。生成式AI通过创建虚构身份缓解隐私问题，但公平性问题依然存在。基于现有的DCFace领先框架，我们提出一种新的受控生成流程，显著提升公平性。通过经典公平性度量以及基于对数几率模型和方差分析的深入统计分析，证明该生成流程在公平性改善方面优于其他偏差缓解方法，同时带来轻微的原始性能提升。
原文摘要 · Abstract (English)
Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities, but fairness problems remain. Using the existing DCFace SOTA framework, we introduce a new controlled generation pipeline that improves fairness. Through classical fairness metrics and a proposed in-depth statistical analysis based on logit models and ANOVA, we show that our generation pipeline improves fairness more than other bias mitigation approaches while slightly improving raw performance.
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